Information processing apparatus and information processing method

ABSTRACT

A report concerning the contents obtained by interpretation based on a medical image can be efficiently created without any constraints of expression. An information processing apparatus according to this invention includes an image analysis unit which acquires information concerning a region name or disease name based on an analysis result on the input medical image, an input unit which inputs the result obtained by interpreting the medical image as character information, a conversion candidate prediction unit which outputs conversion candidates concerning the input character information, and a display control unit which displays the input character information upon converting the character information into character information selected from the conversion candidates. The apparatus further includes a priority level setting unit which sets priority levels in advance for character information output as the conversion candidates.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.12/673,652 filed on Feb. 16, 2010, which is a National Stage Applicationof PCT International Application No. PCT/JP2008/065912 filed on Aug. 28,2008, which claims the benefit of and priority to Japanese PatentApplication No. 2007-256011 filed on Sep. 28, 2007, the entire contentsof each of which are hereby incorporated by reference herein in itsentirety.

TECHNICAL FIELD

The present invention relates to an information processing technique inan information processing apparatus for creating a report concerning aninterpretation result on a medical image.

BACKGROUND ART

In the medical field, the digitization of the medical images obtained byimaging objects has been implemented. This makes it possible tomonitor-display the medical image data output from medical imagingapparatuses such as a CR apparatus, a CT apparatus, an MRI apparatus,and an ultrasonic apparatus at the time of diagnosis. A doctor thenmakes diagnosis by interpreting such monitor-displayed medical imagesand observing the state of a morbid region and its temporal change. Notethat CR, CT, and MRI respectively stand for Computed Radiography,Computed Tomography, and Magnetic Resonance Imaging.

Conventionally, a medical image processing apparatus called acomputer-aided diagnosis apparatus which can automatically detect amorbid region as an abnormal shadow candidate by image analysis onmedical image data has been developed to reduce the load on a doctormaking diagnosis.

This medical image processing apparatus can detect abnormal shadowcandidates such as an abnormal tumor shadow indicating a cancer or thelike and a high-density micro calcification shadow based on inputmedical image data. Automating part of diagnosing operation by thedoctor in this manner can reduce the load on the doctor making diagnosisand improve the accuracy of a diagnosis result.

In making diagnosis based on medical images, the doctor needs to createa report concerning an interpretation result as a diagnosis result inaddition to interpreting a medical image. The operation of creating thisreport also imposes a very heavy load on the doctor.

On the other hand, a medical image processing apparatus which allows tocreate a report concerning an interpretation result by only selecting aform text which is created in advance when interpretation results areinput has been proposed to reduce the load of the operation of creatingsuch reports (see, for example, Japanese Patent Laid-Open No.2004-167087).

In the case of Japanese Patent Laid-Open No. 2004-167087, however, sincea report is created by filling in blanks concerning the respective itemswith selected character strings in the mode defined by a form text,expressions about an interpretation result are limited.

It is also effective to use a so-called input prediction technique ofdisplaying words/sentences, as conversion candidates, concerning acharacter which is being input instead of a form text. The conventionalinput prediction technique is designed to display words with high inputfrequencies and recently input words at higher ranks in conversioncandidates. That is, this technique is not designed to preferentiallydisplay words/sentences suitable for interpreted medical images. Evenif, therefore, the input prediction technique is used, it is notnecessarily possible to efficiently create a report.

DISCLOSURE OF INVENTION

The present invention has been made in consideration of the aboveproblems, and has as its object to allow to efficiently create a reportconcerning an interpretation result on a medical image without anyconstraints of expression. In addition, other objects and features ofthe present invention will be apparent from the following specificationand the drawings.

In order to achieve the above objects, an information processingapparatus according to the present invention has the followingarrangement.

This apparatus is characterized by comprising: image input means forinputting a medical image obtained by imaging an object by using amedical imaging apparatus; and

output means for outputting a plurality of pieces of characterinformation as candidates of character information forming a documentrepresenting a result obtained by interpreting the input medical imageon the basis of an analysis result on the input medical image.

According to the present invention, it is possible to efficiently createa report concerning an interpretation result on a medical image withoutany constraints of expression.

Further features of the present invention will become apparent from thefollowing description of exemplary embodiments with reference to theattached drawings.

BRIEF DESCRIPTION OF DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of the specification, illustrate embodiments of the invention and,together with the description, serve to explain the principles of theinvention.

FIG. 1 is a block diagram showing the arrangement of a medical imageprocessing system including a medical image processing apparatus as aninformation processing apparatus according to the first embodiment ofthe present invention;

FIG. 2 is a block diagram showing the hardware arrangement of themedical image processing apparatus;

FIG. 3 is a block diagram showing the arrangement of the respectivefunctions implemented when a report creation support program isexecuted;

FIG. 4 is a view showing an example of the arrangement of a conversioncandidate table;

FIG. 5 is a flowchart showing the contents of processing in a prioritylevel setting unit;

FIG. 6 is a view showing an example of a conversion candidate table inwhich detection identifiers are set;

FIGS. 7A and 7B are views each showing an example of a UI for reportcreation in the medical image processing apparatus;

FIG. 8 is a view showing an example of a UI for report creation in themedical image processing apparatus;

FIG. 9 is a block diagram showing the functional arrangement of amedical image processing apparatus as an information processingapparatus according to the second embodiment of the present invention;

FIG. 10 is a flowchart showing the contents of processing in aninterpretation state detection unit;

FIG. 11 is a flowchart showing the contents of processing in a prioritylevel setting unit;

FIG. 12 is a view showing an example of a conversion candidate table inwhich detection identifiers are set;

FIG. 13 is a view showing an example of a UI for report creation in themedical image processing apparatus;

FIGS. 14A and 14B are views each showing an example of a medical imagedisplayed on the medical image processing apparatus;

FIG. 15 is a block diagram showing a functional arrangement implementedin a medical image processing apparatus as an information processingapparatus according to the fourth embodiment of the present invention;

FIG. 16 is a block diagram showing a functional arrangement implementedin a medical image processing apparatus as an information processingapparatus according to the fifth embodiment of the present invention;

FIG. 17 is a block diagram showing the arrangement of a medical imageprocessing system including the medical image processing apparatus asthe information processing apparatus according to the fifth embodimentof the present invention; and

FIG. 18 is a view showing an example of display of conversion candidatesin the medical image processing apparatus.

BEST MODE FOR CARRYING OUT THE INVENTION

The best mode for carrying out the present invention will be describedbelow with reference to the accompanying drawings. Note that a medicalimage processing apparatus as an information processing apparatusaccording to each embodiment to be described below displays, asconversion candidates, words/sentences concerning a character input by adoctor in inputting an interpretation result on a medical image. In thiscase, the medical image processing apparatus is characterized bydisplaying conversion candidates on the basis of the priority levels setbased on an analysis result on the medical image.

In the following description of each embodiment, assume that“words/sentences concerning an input (recognized) character” arewords/sentences starting from the input character. Words/sentencesstarting from an input character are extracted from a plurality ofwords/sentences registered in advance in a conversion candidate tablefunctioning as a dictionary and are displayed as “conversioncandidates”.

In addition, “words/sentences concerning a region name or disease name”include a word straightly expressing the region name or disease name, aword expressing a superordinate concept of the region name or diseasename, and a sentence including the region name or disease name. Assumealso that such words/sentences include words/sentences which arecustomarily used when sentences are created by using the region name ordisease name.

First Embodiment

<1. Arrangement of Medical Image Processing System>

FIG. 1 is a block diagram showing the arrangement of a medical imageprocessing system 100 including a medical image processing apparatus asan information processing apparatus according to the first embodiment ofthe present invention.

As shown in FIG. 1, the medical image processing system comprises amedical image processing apparatus 101, a medical imaging apparatus 102,and an image database 103, which are communicatively connected to eachother via a network 104.

The medical imaging apparatus 102 is an apparatus which generatesmedical images by imaging an object, and includes an X-ray device, CT,MRI, PET, ultrasonic diagnosis apparatus, OCT, and the like. Note thatPET stands for Positron Emission Tomography, and OCT stands for OpticalCoherence Tomography.

The image database 103 stores each medical image captured by the medicalimaging apparatus 102 upon addition of management attribute informationsuch as an examination date, hospital name, patient name, birth date,examination type, and examination region.

The medical image processing apparatus 101 is an apparatus fordisplaying a medical image captured by the medical imaging apparatus 102or a medical image stored in the image database 103, inputting, ascharacter information, an interpretation result obtained by a doctorbased on the displayed medical image, and creating a report.

<2. Hardware Arrangement of Medical Image Processing Apparatus 101>

FIG. 2 is a block diagram showing the hardware arrangement of themedical image processing apparatus 101. As shown in FIG. 2, the medicalimage processing apparatus 101 comprises a CPU (Central Processing Unit)201, an input device 203, a display device 204, a memory 202, a disk 205as constituent elements.

The CPU 201 executes various control programs and controls the operationof each constituent element of the medical image processing apparatus101. The input device 203 accepts a pointing input and an input of acharacter or the like by the doctor. The display device 204 displays anexecution result on each type of control program which is obtained bythe CPU 201. The display device 204 includes, for example, a CRT monitorand a liquid crystal monitor.

The memory 202 stores predetermined control programs, and provides awork area at the time of execution of a control program. The disk 205stores various kinds of control programs including an operating system(OS) 206, a device drive 207 for peripheral devices, and a controlprogram (to be referred to as a report creation support program 208) forimplementing an image processing method according to the presentinvention. The disk further stores data and the like used in theexecution of various types of control programs (e.g., a conversioncandidate table 209 (to be described in detail later) used when thereport creation support program 208 is executed).

<3. Functional Arrangement of Medical Image Processing Apparatus 101>

Each function implemented by executing the report creation supportprogram 208 of the medical image processing apparatus 101 (to be simplyreferred to as each function which the medical image processingapparatus 101 has hereinafter) will be described next.

FIG. 3 is a block diagram showing the arrangement of the respectivefunctions which the medical image processing apparatus 101 has. As shownin FIG. 3, each function which the medical image processing apparatus101 has includes an image input unit 301 which inputs the medical imagedata transmitted from the medical imaging apparatus 102 or the imagedatabase 103.

The above functions also include an image analysis unit 302 whichperforms image analysis on input medical image data and specifies aregion name, a disease name, or the like (information concerningdiagnosis of an object) which can be used for input operation of aninterpretation result. The functions further include a priority levelsetting unit 303 which sets priority levels used for display operationof conversion candidates extracted from the words/sentences registeredin the conversion candidate table 209 on the basis of the image analysisresult obtained by the image analysis unit 302.

The above functions further include a conversion candidate storage unit304 which stores the conversion candidate table 209, in which aplurality of words/sentences are registered, in the disk 205 to providewords/sentences concerning the character input via the input device 203and recognized by an input unit 305.

The above functions also include the input unit 305 which recognizes thecharacter input via the input device 203, and a conversion candidateprediction unit 306 which extracts words/sentences concerning thecharacter recognized by the input unit 305 from the conversion candidatetable 209 as conversion candidates. Note that the conversion candidateprediction unit 306 can also discriminate one of the extractedconversion candidates which is actually selected by the doctor and setthe number of times of selection of the selected conversion candidate inthe conversion candidate table 209.

In addition, the above functions include a display control unit 307which performs control to display the conversion candidates extracted bythe conversion candidate prediction unit 306 in accordance with thepriority levels set in correspondence with the conversion candidates.

Furthermore, the above functions include a display unit 308 whichdisplays the character recognized by the input unit 305, displays theconversion candidates extracted by the conversion candidate predictionunit 306 under the control of the display control unit 307, and displaysthe medical image data input by the image input unit 301.

<4. Arrangement of Conversion Candidate Table>

FIG. 4 is a view showing an example of the arrangement of the conversioncandidate table 209. As shown in FIG. 4, in the conversion candidatetable 209, a plurality of words/sentences are registered in “conversioncandidate group” 404 so as to provide words/sentences concerning thecharacter recognized by the input unit 305 as conversion candidates.

In “type” 402, information is registered, which indicates whether eachword/sentence registered in “conversion candidate group” 404 is aword/sentence concerning a region name of the human body, aword/sentence concerning a disease name, a word/sentence concerning thedegree of a disease, or another word/sentence. Assume that “conversioncandidate group” 404 and “type” 402 are registered in the conversioncandidate table 209 before the execution of the report creation supportprogram 208.

The conversion candidate table 209 is configured to set “detectionidentifier” 401 and “selection count” 403 in correspondence with eachword/sentence registered in “conversion candidate group” 404.

“Detection identifier” 401 is an identifier indicating a priority levelwhich is used when each word/sentence registered in “conversioncandidate group” 404 is displayed as a conversion candidate, and is setby the priority level setting unit 303 (this operation will be describedin detail later).

In addition, “selection count” 403 is the number of times of use of aconversion candidate of the conversion candidates extracted by theconversion candidate prediction unit 306 which has been actuallyselected by the doctor and used in report creation, and is set by theconversion candidate prediction unit 306.

The set values set in “detection identifier” 401 will be described next.The set values which are set in “detection identifier” 401 incorrespondence with the words/sentences corresponding to “region”registered in “type” 402 will be described here as an example.

“0000” is set in “detection identifier” 401 associated with aword/sentence, of the words/sentences registered in “conversioncandidate group” 404 of the conversion candidate table 209, whichconcerns the region name of a region which is not included in the inputmedical image data.

“0001” is set in “detection identifier” 401 associated with aword/sentence, of the words/sentences registered in “conversioncandidate group” 404 of the conversion candidate table 209, whichconcerns the region name of a region which is included in the inputmedical image data.

“0011” is set in “detection identifier” 401 associated with aword/sentence which is included in the input medical image data andconcerns the region name of a region in which abnormality has beendetected.

If it is determined during creation of a report that a description abouta specific region name or disease name is complete, “0010” is re-set in“detection identifier” associated with a word/sentence concerning thespecific region name or disease name.

Assume that the priority levels of the values set in “detectionidentifier” 401 in this manner are 0011>0001>0010>0000 in the displaycontrol unit 307.

In this manner, when extracting and displaying conversion candidatesfrom the conversion candidate table for the character input by thedoctor, the medical image processing apparatus according to thisembodiment sets, in advance, priority levels based on analysis resultson medical images for the words/sentences registered in the conversioncandidate table.

With this operation, conversion candidates suitable for a medical imageare preferentially displayed, and the doctor can efficiently create areport.

Note that the number of bits of each set value set in “detectionidentifier” 401 of the conversion candidate table 209 is notspecifically limited, and may be larger or smaller than four bits. Inaddition, the types of set values are not limited to the four types,that is, 0011, 0001, 0010, and 0000.

<5. Function of Image Analysis Unit 302>

The contents of processing in the image analysis unit 302 will bedescribed next. The image analysis unit 302 performs image analysis onthe medical image data input by the image input unit 301, and detects aregion included in the medical image data.

More specifically, if input medical image data is chest CT image data,this unit segments the medical image data into regions, for example, alung field, diaphragm, bronchus, pulmonary artery, and pulmonary vein,and further segments the lung field into regions, for example, a lobussuperior, lobus medius, and lobus inferior. Note, however, that thetypes of regions into which image data is segmented are not limited tothem.

Assume that this embodiment uses the level set method as a kind ofdynamic contour method to detect a region included in medical imagedata. According to the level set method, a level set function higherthan the dimension of a detection target region by one dimension isdefined, and the region to be detected is regarded as a zero contour ofthe function. This function is then updated based on the followingevolution equation called a level set equation to control the contourand detect the region.φt+F|Vφ|=0where φt is the value obtained by calculating the first derivative ofthe level set function in the time axis direction, F is the growth speedof the contour, and |Vφ| is the absolute value of the gradient of thelevel set function.

The above description has exemplified the method of detecting a regionby using the level set method. Other than this method, however, theregion detection method can be, for example, a method based on thresholdprocessing, region growing method, dynamic contour method, clusteringmethod, or minimum graph cut method. It suffices to detect a region byusing one of these techniques or another technique. Alternatively, thesedetection methods can be selectively used in accordance with regions. Inaddition, it suffices to detect a region by using a probability atlas, ahuman figure model, or the like as pre-knowledge as well as using imagefeature amounts.

The image analysis unit 302 further specifies the region name of thedetected region by collating with medical knowledge. If, for example,the medical image data is medical image data about a chest portion,region names such as a pulmonary lung, segment, bronchus, lymph gland,and artery/vein are specified.

The image analysis unit 302 further detects an abnormality such as lungcancer from the region in addition to specifying the above region name.

A method of detecting an abnormality can be, for example, filterprocessing for detecting an abnormality, pattern matching processing,abnormality detection processing using a discriminator, or theprocessing of detecting the difference between a past image, an averageshape image, or the like and a medical image as a target byregistration. The image analysis unit 302 detects an abnormality byusing one of these techniques or another technique.

In addition, the image analysis unit 302 performs disease classificationand benignity/malignity discrimination for a detected abnormality.Assume that a discriminator such as a support vector machine, AdaBoost,Bayesian discriminator, or neuralnet is used for disease classificationand benignity/malignity discrimination for a detected abnormality. Notethat the discriminator to be used is not limited to them.

<6. Function of Priority Level Setting Unit 303>

The contents of processing in the priority level setting unit 303 willbe described next with reference to FIG. 6 using the flowchart of FIG.5.

In step S501, the CPU 201 acquires the region names specified by theimage analysis unit 302.

In step S502, the CPU 201 searches the conversion candidate table 209 todiscriminate words/sentences concerning each region name acquired instep S501 (words/sentences corresponding to “region” in “type” 402). TheCPU 201 sets “0001” in “detection identifier” 401 associated with thediscriminated word/sentence.

Assume that the medical image data includes regions such as an inferiorphrenic lymph node, descending aorta, lobus inferior S7, bronchus,tracheobronchial lymph node, thoracic vertebra, and pleura. As shown inFIG. 6, therefore, “0001” is set in “detection identifier” 401associated with words/sentences concerning these region names in theconversion candidate table 209 (see “601”). Note that a liver and aliver artery are regions which are not included in the medical imagedata. For this reason, in the case shown in FIG. 6, “0000” remains thesame in “detection identifier” 401 associated with words/sentencesconcerning these region names (see “601′”).

In step S503, the CPU 201 causes a branch depending on whether the imageanalysis unit 302 has detected an abnormality. If the image analysisunit 302 has detected no abnormality, the CPU 201 terminates theprocessing in the priority level setting unit 303.

If the image analysis unit 302 has detected an abnormality, the processadvances to step S504. In step S504, the CPU 201 sets “0011” in“detection identifier” 401 associated with a word/sentence concerningthe region name of the region in which the abnormality has been detected(a word/sentence corresponding to “region” in “type” 402) to increasethe priority level of the word/sentence.

Assume that an abnormality has been detected in a lobus inferior (right)S6 of the regions included in the medical image data. As a consequence,as shown in FIG. 6, “0011” is set in “detection identifier” 401associated with the lobus inferior (right) S6 and lobus inferiorsincluding the lobus inferior (right) S6 (see “602”).

In step S505, the CPU 201 acquires the disease name detected by theimage analysis unit 302, and searches the conversion candidate table 209based on the disease name. If there is any word/sentence concerning thedisease name (the word/sentence corresponding to “disease” in “type”402), the CPU 201 sets a predetermined set value in “detectionidentifier” 401 associated with the word/sentence.

More specifically, the CPU 201 sets “0001” in “detection identifier” 401associated with the word/sentence concerning the acquired disease name(the word/sentence corresponding to “disease” in “type” 402).

Note that the CPU 201 sets “0000” in “detection identifier” 401associated with a word/sentence concerning the acquired disease name (aword/sentence corresponding to “disease” in “type” 402) if theword/sentence concerns the disease which can develop only in an organarea other than the organ area in which the disease has developed.

The CPU 201 also sets “0000” in “detection identifier” 401 associatedwith a word/sentence (a word/sentence corresponding to “disease” in“type” 402) other than those concerning the acquired disease name.

Assume that the organ area is a chest portion, and the disease nameacquired in step S505 is a nodular hepatic cirrhosis. In this case, asshown in FIG. 6, “0000” is kept set in “detection identifier” 401corresponding to a word concerning a disease name (e.g., a nodularhepatic cirrhosis) which can develop only in an abdominal portion orhead portion (see “603”).

On the other hand, “0001” is set in “detection identifier” 401corresponding to a word/sentence concerning the acquired disease name(e.g., a nodular tuberculosis or tuberculosis) (see “604”).

In step S506, the CPU 201 sets “0011” in “detection identifier” 401corresponding to another word/sentence (a word/sentence corresponding to“degree” in “type” 402) concerning the region name acquired in step S501and the disease name acquired in step S505.

If, for example, an unclearly demarcated node is detected in the lobusinferior (right) S6, the CPU 201 sets “0011” in “detection identifier”401 corresponding to words like “unclearly demarcated”, “light”, and“irregular” as words/sentences indicating the degree of the disease (see“605”).

In addition, the CPU 201 sets “0011” in “detection identifier” 401associated with words/sentences like “recognize” and “can see”(words/sentences corresponding to “others” in “type” 402) which concernthe acquired region name or disease name (see “606”).

<7. Function of Conversion Candidate Prediction Unit 306>

The contents of processing in the conversion candidate prediction unit306 will be described next. In the conversion candidate prediction unit306, the CPU 201 searches the conversion candidate table 209 stored inthe conversion candidate storage unit 304 and reads out conversioncandidates concerning the character recognized by the input unit 305.

A method of reading out conversion candidates will be described belowwith reference to FIGS. 7A and 7B.

FIGS. 7A and 7B are views each showing an example of a UI for reportcreation in the medical image processing apparatus 101. Assume that thedoctor has input the character “1” in a report column 701 displayed onthe display device 204 of the medical image processing apparatus 101.

In this case, if conversion candidates of the character (words/sentencesstarting from “1”) are read out from the conversion candidate table 209in descending order of selection counts as in the prior art, liver,lobus inferior (right) S7, liver artery, lobus inferior (left) S6, lobusinferior (right) S6, and the like are read out in the order named.

In contrast to this, the conversion candidate prediction unit 306 readsout and arranges conversion candidates based on the priority levels ofthe set values set in “detection identifier” 401 and “selection count”403 instead of reading out conversion candidates based on the selectioncounts and the order in which they have currently been read out.

In this case, since the priority levels of the set values set in“detection identifier” are 0011>0001>0010>0000, lobus inferior (right)S6, lobus inferior, lobus inferior (right) S7, lobus inferior (left) S6,. . . , liver, liver artery, . . . are read out in the order named inthe example of FIG. 7A. The display unit 308 displays the conversioncandidates read out from the conversion candidate table 209 by thedisplay control unit 307.

Note that a method of displaying readout conversion candidates can be amethod of displaying the conversion candidates in a pull-down menu formor pop-up form near the cursor of the input character as indicated by“702” in FIGS. 7A and 7B, or a method of displaying the conversioncandidates in another window.

Assume also that the display position of conversion candidates can beset to an upper, lower, right, or left position on the window, and thedoctor can arbitrarily set a display size. Assume that the number ofconversion candidates to be displayed can be arbitrarily set.Furthermore, conversion candidates can be displayed in a sentence forminstead of on a word basis, as shown in FIG. 7B.

Upon detecting at least one of the region names or disease names writtenin the report column 701 by the doctor, the conversion candidateprediction unit 306 determines that an interpretation result concerningthe detected region name or disease name has already been written(converted) as a report.

Note that a method of determining whether an interpretation result hasbeen written can be a method of determining that an interpretationresult has been written when the corresponding region name or diseasename has been used once, or a method of determining that aninterpretation result has been written upon detecting a paragraph orpunctuation mark. Alternatively, it suffices to use a method ofdetermining that an interpretation result has been written uponrecognizing the meaning of a sentence by syntactic analysis/semanticanalysis.

Upon determining that the interpretation result has been written, theconversion candidate prediction unit 306 sets “0010” in “detectionidentifier” 401 associated with a word/sentence concerning the writtenregion name or disease name which is registered in the conversioncandidate table 209.

This decreases the priority level of the word/sentence concerning theregion name or disease name which has already been written in the reportas the interpretation result. As a consequence, as shown in FIG. 8, aword/sentence concerning a region name or disease name which has notbeen written in the report column 701 is displayed at a higher positionin a pull-down menu 801.

Note that the conversion candidate prediction unit 306 may be configuredto extract words/sentences concerning the character recognized by theinput unit 305 upon narrowing down them in accordance with anexamination purpose, for example, screening, detailed examination, orfollow-up, instead of extracting all the words/sentences.

In this case, it suffices to separately form conversion candidate tablescorresponding to examination purposes and extract conversion candidatesby searching the conversion candidate tables.

Alternatively, it suffices to form a common conversion candidate table,set in advance specific set values to detection identifiers associatedwith words/sentences matching examination purposes, and extract onlyconversion candidates corresponding to detection identifiers to whichspecific set values are set. Note that words/sentences matchingexamination purposes are, for example, words/sentences expressing anincrease/decrease in the number of nodes or a change in the size ofnodes, which indicate the stage of development of a disease(words/sentences corresponding to “others” in “type” 402).

As is obvious from the above description, the medical image processingapparatus according to this embodiment is configured to displayconversion candidates on the basis of the priority levels set inaccordance with an analysis result on a medical image when displayingwords/sentences concerning an input character as conversion candidates.

This makes it possible to display conversion candidates which are likelyto be selected. The doctor can therefore efficiently create a reportconcerning an interpretation result on the basis of a medical imagewithout any constraints of expression.

Second Embodiment

The first embodiment has exemplified the case in which one medical imagedata is input by the image input unit. However, the medical imageprocessing apparatus according to the present invention is not limitedto the case in which one medical image data is input, and can be appliedto a case in which a plurality of medical image data are simultaneouslyinput. The case in which a plurality of medical image data issimultaneously input includes, for example, a case in which consecutivemedical image data at different slice positions in the same organ areaare input.

As described above, when a plurality of medical image data aresimultaneously input, different region names or disease names may bedetected from the respective medical image data as a result of imageanalysis.

For this reason, this embodiment is configured to manage which medicalimage the doctor currently interprets and to increase (preferentiallydisplay) the priority level of a word/sentence concerning a region nameor disease name detected from the medical image which the doctorcurrently interprets. The embodiment is further configured to managemedical images which the doctor has already interpreted and decrease(non-preferentially display) the priority level of a word/sentenceconcerning a region name or disease name detected from a medical imagewhich the doctor has already interpreted.

The medical image processing apparatus according to this embodiment willbe described in detail below.

<1. Functional Arrangement of Medical Image Processing Apparatus>

FIG. 9 is a block diagram showing the functional arrangement of themedical image processing apparatus as an information processingapparatus according to the second embodiment of the present invention.The functional arrangement of the medical image processing apparatusaccording to this embodiment is basically the same as that of themedical image processing apparatus according to the first embodiment.Note, however, the functions implemented in the medical image processingapparatus according to this embodiment further include an interpretationstate detection unit 901 which detects the interpretation state of adoctor.

<2. Function of Interpretation State Detection Unit>

The contents of processing in the interpretation state detection unit901 will be described with reference to the flowchart of FIG. 10. Instep S1001, a CPU 201 acquires all the region names or disease namesspecified by an image analysis unit 302. With this operation, all theregion names/disease names included in a plurality of input medicalimage data are acquired.

In step S1002, the CPU 201 acquires the interpretation state of thedoctor (the medical images which the doctor has interpreted). Morespecifically, if a plurality of slice images of CT data is input asmedical images, the CPU 201 acquires a slice No. history as medicalimages which the doctor has interpreted.

In step S1003, the CPU 201 determines whether any region name or diseasename is detected from the medical image which the doctor currentlyinterprets. If the CPU 201 determines that a region name or disease nameis detected from the medical image which the doctor currentlyinterprets, the process advances to processing in a priority levelsetting unit 902. This allows the priority level setting unit 902 tosequentially recognize region names or disease names detected from themedical image, of a plurality of medical images, which the doctorcurrently interprets.

If the CPU 201 determines that no region name or disease name isdetected from the medical image which the doctor currently interprets,the process advances to step S1004. In step S1004, the CPU 201determines whether the doctor has interpreted all the medical images. Ifthe CPU 201 determines that the doctor has not interpreted all themedical images, the process returns to step S1002. If the CPU 201determines that the doctor has interpreted all the medical images, theCPU 201 terminates the processing in the interpretation state detectionunit 901.

<3. Function of Priority Level Setting Unit 902>

The contents of processing in the priority level setting unit 902 willbe described next with reference to FIG. 12 along the flowchart of FIG.11. FIG. 11 is a flowchart showing a processing procedure in thepriority level setting unit 902. FIG. 12 is a view showing an example ofa conversion candidate table in a state in which the doctor hascompleted interpretation on the medical image data of a chest portionwhen the medical image data includes the organ area from the chestportion to the abdominal portion, and the image analysis unit 302 hasdetected abnormalities in the chest portion and the abdominal portion.

In step S1101, the CPU 201 acquires the region name acquired by theinterpretation state detection unit 901.

In step S1102, the CPU 201 searches the conversion candidate table 209and sets a predetermined set value in “detection identifier” 401associated with a word/sentence (a word/sentence corresponding to“region” in “type” 402) concerning the region name acquired in stepS1101.

More specifically, as shown in FIG. 12, the CPU 201 sets “0101” to adetection identifier associated with a word/sentence which is includedin the input medical image data and concerns a region name which thedoctor has not interpreted (see “1201”).

In addition, the CPU 201 sets “0001” to a detection identifierassociated with a word/sentence which is included in the medical imagedata and concerns a region name which the doctor has already interpreted(see “1202”).

On the other hand, the CPU 201 sets “0000” to a detection identifierassociated with a word/sentence concerning a region name which is notincluded in the input medical image data.

Subsequently, in step S1103, the CPU 201 causes a branch depending onwhether the image analysis unit 302 has detected any abnormality. If theimage analysis unit 302 has detected no abnormality, the CPU 201terminates the processing in the priority level setting unit 902.

If the image analysis unit 302 has detected an abnormality, the CPU 201sets a predetermined set value to a detection identifier associated witha word/sentence concerning the region name of a region in which anabnormality is included (a word/sentence corresponding to “region” in“type” 402) in step S1104.

More specifically, as shown in FIG. 12, the CPU 201 sets “0111” to adetection identifier associated with a word/sentence concerning theregion name of a region in which the image analysis unit 302 hasdetected an abnormality and which the doctor has not interpreted (see“1203”).

The CPU 201 also sets “0011” to a detection identifier associated with aword/sentence concerning the region name of a region in which the imageanalysis unit 302 has detected an abnormality and which the doctor hasalready interpreted (see “1204”).

In addition, in step S1105, the CPU 201 acquires the disease namedetected by the image analysis unit 302 in image analysis, and searchesa conversion candidate table 209 based on the acquired disease name. TheCPU 201 then sets a predetermined set value to a detection identifierassociated with a word/sentence concerning the acquired disease name (aword/sentence corresponding to “disease” in “type” 402).

More specifically, the CPU 201 sets a set value in the following manner:

(1) In the case of a word/sentence concerning an acquired disease name(a word/sentence corresponding to “disease” in “type” 402) which isincluded in the medical image data which the doctor currentlyinterprets:

if the disease name has already been read out, the CPU 201 sets “0001”to a detection identifier associated with a word/sentence concerning thedisease name (see “1205”); and if the disease name has not been readout, the CPU 201 sets “0101” to a detection identifier associated with aword/sentence concerning the disease name (see “1206”).

(2) In the case of a word/sentence concerning an acquired disease name(a word/sentence corresponding to “disease” in “type” 402) which is notincluded in the medical image data which the doctor currentlyinterprets:

if the disease name has already been read out, the CPU 201 sets “0011”to a detection identifier associated with a word/sentence concerning thedisease name (see “1207”); and if the disease name has not been readout, the CPU 201 sets “0111” to a detection identifier associated with aword/sentence concerning the disease name (see “1208”).

(3) In the case of a word/sentence other than those concerning theacquired disease name (a word/sentence corresponding to “disease” in“type” 402):

the CPU 201 sets “0000” to a detection identifier associated with thisword/sentence.

Assume that the priority levels of the set values set in “detectionidentifier” 401 are 0011>0001>0010>0111>0101>0000 in the display controlunit 307.

In step S1206, the CPU 201 sets a predetermined set value in “detectionidentifier” 401 associated with a word/sentence other than thoseconcerning the acquired region name or disease name (a word/sentencecorresponding to “degree” or “others” in “type” 402).

A case in which an unclearly demarcated node is detected in a lobusinferior will be described as an example. In this case, the CPU 201 sets“0011” in “detection identifier” 401 associated with a word/sentenceother than a region name/disease name, for example, “unclearlydemarcated”, “recognize”, “can see”, and “suspect”, and also associatedwith the acquired region name or disease name (see “1209”).

<4. Function of Conversion Candidate Prediction Unit 903>

The contents of processing in a conversion candidate prediction unit 903will be described next. In the conversion candidate prediction unit 903,the CPU 201 searches the conversion candidate table 209 stored in aconversion candidate storage unit 304 and reads out conversioncandidates of the character recognized by an input unit 305.

A method of reading out conversion candidates will be described belowwith reference to FIG. 13.

Assume that the doctor has input the character “1” in a report column1300 displayed on a display device 204 of a medical image processingapparatus 101.

In this case, if conversion candidates of the character (words/sentencesstarting from “1”) are read out from the conversion candidate table 209in descending order of selection counts as in the prior art, liver,lobus inferior (right) S7, liver artery, lobus inferior (left) S6, lobusinferior (right) S6, and the like are read out in the order named.

In contrast to this, the conversion candidate prediction unit 903 readsout and arranges conversion candidates based on the priority levels ofthe set values set in “detection identifier” 401 and “selection count”403 instead of reading out conversion candidates on the basis of theselection counts and the order in which they have currently been readout.

In this case, the priority levels of the set values set in “detectionidentifier” 401 are 0011>0001>0010>0111>0101>0000. Consequently, lobusinferior (right) S6, lobus inferior, lobus inferior (right) S7, lobusinferior (left) S6, . . . , liver, liver artery, . . . are read out inthe order named in the example in FIG. 13. A display unit 308 displaysthe conversion candidates read out from the conversion candidate table209 by the display control unit 307.

Although the image analysis unit 302 has detected abnormalities in thechest and abdominal portions, since the doctor has started creating areport before he/she has seen the abnormality in the abdominal portion,words/sentences concerning the chest portion are extracted as conversioncandidates of the character “1”.

As described above, the medical image processing apparatus according tothis embodiment comprises the interpretation state detection unit whichdetects the interpretation state of the doctor (a medical image whichthe doctor currently interprets).

With this arrangement, even if a plurality of medical image data issimultaneously input, conversion candidates which are likely to beselected can be displayed in consideration of the medical image whichthe doctor currently interprets. The doctor can therefore efficientlycreate a report concerning an interpretation result on the basis of amedical image without any constraints of expression.

Third Embodiment

According to the first and second embodiments described above, thedisplay condition for medical images input by the image input unit anddisplayed by the display unit remains constant. However, the medicalimage processing apparatus as the information processing apparatusaccording to the present invention can display medical image data undervarious display conditions instead of limiting the number of displayconditions for the display of medical images to one. Assume that displayconditions are separately set based on the characteristics of eachmedical imaging apparatus and the set conditions for imaging operationin each hospital.

When medical images are displayed under various display conditions, theregion names or disease names to be actually displayed differ dependingon display conditions even with respect to the same medical image data.

In the case of chest CT data, for example, as shown in FIG. 14A, thelung field is displayed in black under a mediastinum condition which isset to mainly display soft tissues such as a mediastinum and a chestwall.

In contrast, as shown in FIG. 14B, under a lung field condition which isset to mainly display a lung field, a morbid region of the lung fieldand pulmonary blood vessels are displayed clearly, but all the softtissues of the mediastinum are displayed in white.

For this reason, in this embodiment, the interpretation state detectionunit 901 described above is configured to further manage displayconditions at the time of interpretation of medical images. Theinterpretation state detection unit 901 is also configured to determinea region name or disease name, of the region names or disease namesdetected from the medical image by image analysis, which can bedisplayed under an actual display condition, and to increase thepriority level of a word/sentence concerning the determined region nameor disease name. The contents of processing in the medical imageprocessing apparatus according to this embodiment will be describedbelow.

The contents of processing in an image analysis unit 302, interpretationstate detection unit 901, and priority level setting unit 902 in themedical image processing apparatus according to this embodiment arebasically the same as those in the second embodiment.

One of 0011, 0001, 0010, 0111, 0101, and 0000 is set in “detectionidentifier” 401 of a conversion candidate table 209.

In this embodiment, the interpretation state detection unit 901acquires, as the interpretation state of the doctor, not only a sliceNo. but also a luminance condition linked with the slice No. Inaddition, when a medical image is displayed under the luminancecondition (display condition), the interpretation state detection unit901 determines a region name or disease name which can be displayed onthe medical image.

The interpretation state detection unit 901 performs this determinationevery time a medical image is displayed, and specifies a region name ordisease name determined as the one that can be displayed on the medicalimage. In addition, the interpretation state detection unit 901 re-setsa predetermined set value in “detection identifier” 401 associated witha word/sentence concerning the specified region name or disease name.

More specifically, the interpretation state detection unit 901 sets theset value by replacing the most significant digit of the set value whichhas already been set, that is, “0”, with “1” in “detection identifier”401 associated with a word/sentence concerning the specified region nameor disease name.

As a result, one of the set values, that is, 1011, 1001, 1010, 1111, and1101, is added to “detection identifier” 401 of the conversion candidatetable 209.

Assume that the priority levels of the set values set in “detectionidentifier” 401 are1011>0011>1001>0001>1010>0010>1111>0111>1101>0101>0000 in a displaycontrol unit 307.

As is obvious from the above description, this embodiment can extractregion names/disease names which the doctor has actually seen ininterpretation, by taking consideration of a display condition as wellas a slice No.

Fourth Embodiment

A medical image processing apparatus which learns the descriptionpattern of the doctor and displays conversion candidates on the basis ofthe learning result will be described next as the fourth embodiment ofthe present invention.

FIG. 15 is a block diagram showing the arrangement of the respectivefunctions which a medical image processing apparatus as an informationprocessing apparatus according to the fourth embodiment of the presentinvention has. As shown in FIG. 15, the basic arrangement of thefunctions of the medical image processing apparatus according to thisembodiment is the same as that of the functions of the medical imageprocessing apparatus according to the first embodiment. However, inaddition to the functions which the medical image processing apparatusaccording to the first embodiment has, the fourth embodiment includes alearning unit 1501 which learns the description pattern of the doctor inreport creation.

Including the learning unit 1501, the medical image processing apparatusaccording to this embodiment can learn a writing style and writingtendencies from an overall syntactic viewpoint when a doctor createsreports.

A writing style includes, for example, the habit of starting writingfrom a region or a disease. Writing tendencies from an overall syntacticviewpoint include the order of regions in writing an interpretationresult.

Note that the learning unit 1501 learns a grammatical rule among wordsby performing syntactic analysis and semantic analysis on sentencesinput by the doctor. The apparatus then reads out conversion candidatesby using detection identifiers set based on a grammatical rule for eachdoctor and the numbers of times of selection of conversion candidates.

As is obvious from the above description, the medical image processingapparatus according to this embodiment comprises the learning unit whichlearns an input rule. This makes it possible to accurately extract aconversion candidate which the doctor wants to write. As a consequence,the doctor can efficiently create a report concerning the contents ofinterpretation based on a medical image without any constraints ofexpression.

Fifth Embodiment

In the first embodiment described above, the image analysis unit in themedical image processing apparatus analyzes input medical image data.However, the present invention is not limited to this. For example, theimage analysis unit may be implemented by an external device, and themedical image processing apparatus may be configured to receive theimage analysis result obtained by the external device.

FIG. 16 is a block diagram showing a functional arrangement implementedby a medical image processing apparatus as an information processingapparatus according to the fifth embodiment of the present invention.The functions implemented in the medical image processing apparatusaccording to this embodiment include an image analysis input unit 1601which inputs the image analysis result transmitted from the externaldevice which analyzes the medical image acquired by a medical imagingapparatus.

The image analysis result transmitted from the external device whichanalyzes medical images includes medical image data and at least one ofa region name, a disease name, the degree of the disease, and the stageof development of the disease included in the medical image data.

Note that the image analysis input unit 1601 can be configured to inputthe image analysis result obtained outside the medical image processingapparatus by using a network, or can be configured to read out and inputan image analysis result stored in a storage medium connected to themedical image processing apparatus.

FIG. 17 is a block diagram showing how a medical image processingapparatus 101 and an external device 1701 are connected via a network.

The medical image processing apparatus 101 displays conversioncandidates by using the image analysis result transmitted from theexternal device 1701. This makes it possible to display conversioncandidates which are likely to be selected, even if the medical imageprocessing apparatus does not include an image analysis unit. The doctorcan therefore efficiently create a report concerning the contents ofinterpretation based on a medical image without any constraints ofexpression.

Sixth Embodiment

The first to fifth embodiments each have exemplified the case in whichwords/sentences concerning the character input by the doctor aredisplayed in a pull-down menu form in report creation. However, thepresent invention is not limited to this.

As shown in FIG. 18, for example, conversion candidates which are likelyto be selected may be sequentially displayed in a list in a standardizedform based on an image analysis result.

Other Embodiments

The present invention may be applied to a system constituted by aplurality of devices (e.g., a host computer, an interface device, areader, a printer, and the like) or an apparatus comprising a singledevice (e.g., a copying machine, a facsimile apparatus, or the like).

The object of the present invention is implemented even by supplying astorage medium storing software program codes for implementing thefunctions of the above embodiments to a system or apparatus. In thiscase, the above functions are implemented by causing the computer (or aCPU or an MPU) of the system or apparatus to read out and execute theprogram codes stored in the storage medium. In this case, the storagemedium storing the program codes constitutes the present invention.

As a storage medium for supplying the program codes, a floppy(registered trademark) disk, a hard disk, an optical disk, amagnetooptical disk, a CD-ROM, a CD-R, a magnetic tape, a nonvolatilememory card, a ROM, or the like can be used.

As is obvious, the functions of the above embodiments are implementednot only when the readout program codes are executed by the computer butalso when the OS (Operating System) running on the computer performspart or all of actual processing based on the instructions of theprogram codes.

The functions of the above embodiments are also implemented when theprogram codes read out from the storage medium are written in the memoryof a function expansion board inserted into the computer or a functionexpansion unit connected to the computer, and the CPU of the functionexpansion board or function expansion unit performs part or all ofactual processing based on the instructions of the program codes.

While the present invention has been described with reference toexemplary embodiments, it is to be understood that the invention is notlimited to the disclosed exemplary embodiments. The scope of thefollowing claims is to be accorded the broadest interpretation so as toencompass all such modifications and equivalent structures andfunctions.

The invention claimed is:
 1. An information processing apparatus, whichis communicably connected to a database, for creating a reportconcerning an interpretation result on a medical image, the informationprocessing apparatus comprising: a processor; and a memory storinginstructions that, when executed by the processor, cause the informationprocessing apparatus to: receive a medical image obtained by imaging anobject by using a medical imaging apparatus from the database; receiveregion information found in the received medical image; input at leastone first character relating to the medical image; determine a firsttext candidate and a second text candidate from a plurality of textcandidates in a candidate table, based on the at least one firstcharacter, wherein the first text candidate describes a first region,and the second text candidate describes a second region, wherein acharacter length of the first text candidate and a character length ofthe second text candidate are longer than a character length of the atleast one first character; determine an interpretation status for thefirst region and the second region, wherein the interpretation statusdoes not include information representing whether or not the first textcandidate and the second text candidate were selected, and wherein theinterpretation status indicates whether the medical image has beeninterpreted; determine, based on the determined interpretation status, apriority level for the first text candidate and the second textcandidate, wherein if the first region has already been interpreted andthe second region has not yet been interpreted, a priority level for thefirst text candidate is determined to be higher than a priority levelfor the second text candidate; and visually present, based on thedetermined priority levels for the first text candidate and the secondtext candidate, the first text candidate and the second text candidatein this order.
 2. The information processing apparatus according toclaim 1, wherein the first text candidate and the second text candidateinclude a plurality of characters for conversion of the at least onefirst character.
 3. The information processing apparatus according toclaim 2, wherein the memory stores further instructions that, whenexecuted by the processor, cause the information processing apparatus toset priority levels to preferentially present the plurality ofcharacters as the first text candidate and the second text candidate,which concern a result of an analysis of the medical image.
 4. Theinformation processing apparatus according to claim 3, wherein theplurality of characters to be presented as the first text candidate andthe second text candidate is at least one of a region name included inthe medical image.
 5. The information processing apparatus according toclaim 3, wherein the information processing apparatus arranges theplurality of characters concerning the at least one first character, andpresents the plurality of characters as the first text candidate and thesecond text candidate on the basis of the set priority levels and anumber of times of presentation in the past.
 6. The informationprocessing apparatus according to claim 3, wherein the informationprocessing apparatus sets in advance the priority levels for theplurality of characters to be non-preferentially presented as the firsttext candidate and the second text candidate.
 7. The informationprocessing apparatus according to claim 3, wherein the memory storesfurther instructions that, when executed by the processor, cause theinformation processing apparatus to recognize the medical image of aplurality of medical images and to recognize a display condition for themedical image, and wherein the information processing apparatus sets thepriority levels to preferentially present the plurality of characters ofthe medical image, which are included in the display condition.
 8. Theinformation processing apparatus according to claim 1, wherein themedical image comprises a plurality of images, and wherein a prioritylevel of a text candidate concerning the region information and adisease name, which is from the plurality of images, is increased. 9.The information processing apparatus according to claim 1, wherein eachof the first text candidate, and the second text candidate includes aword and a phrase.
 10. A method for creating a report concerning aninterpretation result on a medical image, the method comprising:receiving a medical image obtained by imaging an object by using amedical imaging apparatus from a database; receiving region informationfound in the medical image; inputting at least one first characterrelating to the medical image; determining a first text candidate and asecond text candidate from a plurality of text candidates in a candidatetable, based on the at least one first character, wherein the first textcandidate describes a first region, and the second text candidatedescribes a second region, wherein a character length of the first textcandidate and a character length of the second text candidate are longerthan a character length of the at least one first character; determiningan interpretation status for the first region and the second region,wherein the interpretation status does not include information relatingwhere or not the first text candidate and the second text candidate wereselected, and wherein the interpretation status indicates whether themedical image has been interpreted; determining, based on the determinedinterpretation status, a priority level for the first text candidate andthe second text candidate, wherein if the first region has already beeninterpreted and the second region has not yet been interpreted, apriority level for the first text candidate is determined to be higherthan a priority level for the second text candidate; and visuallypresenting, based on the determined priority levels for the first textcandidate and the second text candidate, the first text candidate andthe second text candidate in this order.
 11. The method according toclaim 10, wherein the first text candidate and the second text candidateinclude a first plurality of characters for conversion of the at leastone first character.
 12. The method according to claim 11, furthercomprising setting priority levels to preferentially present the firstplurality of characters as the first text candidate and the second textcandidate, which concern a result of an analysis of the medical image.13. A non-transitory computer-readable storage medium storing a programwhich executes a method for creating a report concerning aninterpretation result on a medical image, the method comprising:receiving a medical image obtained by imaging an object by using amedical imaging apparatus from a database; receiving region informationfound in the medical image; inputting at least one first characterrelating to the medical image; determine a first text candidate and asecond text candidate from a plurality of text candidates in a candidatetable, based on the at least one first character, wherein the first textcandidate describes a first region, and the second text candidatedescribes a second region, wherein a character length of the first textcandidate and a character length of the second text candidate are longerthan a character length of the at least one first character; determiningan interpretation status for the first region and the second region,wherein the interpretation status does not include information relatingwhere or not the first text candidate and the second text candidate wereselected, and wherein the interpretation status indicates whether themedical image has been interpreted; determining, based on the determinedinterpretation status, a priority level for the first text candidate andthe second text candidate, wherein if the first region has already beeninterpreted and the second region has not yet been interpreted, apriority level for the first text candidate is determined to be higherthan a priority level for the second text candidate; and visuallypresenting, based on the determined priority levels for the first textcandidate and the second text candidate, the first text candidate andthe second text candidate in this order.